This course teaches you the fundamentals of computational phenotyping, a biomedical informatics method for identifying patient populations. In this course you will learn how different clinical data types perform when trying to identify patients with a particular disease or trait. You will also learn how to program different data manipulations and combinations to increase the complexity and improve the performance of your algorithms. Finally, you will have a chance to put your skills to the test with a real-world practical application where you develop a computational phenotyping algorithm to identify patients who have hypertension. You will complete this work using a real clinical data set while using a free, online computational environment for data science hosted by our Industry Partner Google Cloud.
提供：
400 登録済み
このコースについて
4,732 最近の表示
学習内容
Create a computational phenotyping algorithm
Assess algorithm performance in the context of analytic goal.
Create combinations of at least three data types using boolean logic
Explain the impact of individual data type performance on computational phenotyping.
シラバス - 本コースの学習内容
週
12時間で修了
Introduction: Identifying Patient Populations
Learn about computational phenotyping and how to use the technique to identify patient populations.
5件のビデオ （合計23分）, 9 readings, 2 quizzes
5件のビデオ
Introduction to Computational Phenotyping5 分
Introduction to Manual Record Review4 分
Manual Record Review: Selecting Reviewers and Records6 分
Manual Record Review: Tools and Techniques5 分
9件の学習用教材
Introduction to Specialization Instructors5 分
Course Policies5 分
Accessing Course Data and Technology Platform15 分
Introduction to Course Example15 分
Introduction to Manual Record Review10 分
Methods - Selecting Reviewers10 分
Methods - Selecting Records for Review10 分
Methods - Creating Review Instruments and Protocols10 分
Methods - Assessing Review Quality10 分
2の練習問題
Week 1 Practice Quiz8 分
Week 1 Assessment16 分
週
23時間で修了
Tools: Clinical Data Types
Understand how different clinical data types can be used to identify patient populations. Begin developing a computational phenotyping algorithm to identify patients with type II diabetes.
5件のビデオ （合計19分）, 2 readings, 2 quizzes
5件のビデオ
Computational Phenotyping: Billing Data5 分
Computational Phenotyping: Laboratory Data3 分
Computational Phenotyping: Clinical Observations2 分
Computational Phenotyping: Medications3 分
2件の学習用教材
Testing Individual Data Types30 分
Note about the Assessment2 分
2の練習問題
Programming Exercises Practice Quiz30 分
Week 2 Assessment18 分
週
33時間で修了
Techniques: Data Manipulations and Combinations
Learn how to manipulate individual data types and combine multiple data types in computational phenotyping algorithms. Develop a more sophisticated computational phenotyping algorithm to identify patients with type II diabetes.
2件のビデオ （合計15分）, 2 readings, 2 quizzes
2件の学習用教材
Data Manipulations30 分
Data Combinations45 分
2の練習問題
Programming Exercises Practice Quiz30 分
Week 3 Assessment25 分
週
41時間で修了
Techniques: Algorithm Selection and Portability
Understand how to select a single "best" computational phenotyping algorithm. Finalize and justify a phenotyping algorithm for type II diabetes.
1件のビデオ （合計4分）, 1 reading, 1 quiz
1件のビデオ
1件の学習用教材
Assessing Algorithmic Accuracy, Complexity, and Portability25 分
1の練習問題
Week 4 Assessment20 分
週
54時間で修了
Practical Application: Develop a Computational Phenotyping Algorithm to Identify Patients with Hypertension
Put your new skills to the test - develop an computational phenotyping algorithm to identify patients with hypertension.
1 reading, 1 quiz
1件の学習用教材
Welcome to Practical Applications!5 分
4.9
2件のレビュー人気のレビュー
This is a well-presented course. I highly recommend.
講師
Laura K. Wiley, PhD
Assistant ProfessorDivision of Biomedical Informatics and Personalized Medicine, Anschutz Medical Campus
University of Colorado Systemについて
The University of Colorado is a recognized leader in higher education on the national and global stage. We collaborate to meet the diverse needs of our students and communities. We promote innovation, encourage discovery and support the extension of knowledge in ways unique to the state of Colorado and beyond.
Clinical Data Scienceの専門講座について
Are you interested in how to use data generated by doctors, nurses, and the healthcare system to improve the care of future patients? If so, you may be a future clinical data scientist!
This specialization provides learners with hands on experience in use of electronic health records and informatics tools to perform clinical data science. This series of six courses is designed to augment learner’s existing skills in statistics and programming to provide examples of specific challenges, tools, and appropriate interpretations of clinical data.
By completing this specialization you will know how to: 1) understand electronic health record data types and structures, 2) deploy basic informatics methodologies on clinical data, 3) provide appropriate clinical and scientific interpretation of applied analyses, and 4) anticipate barriers in implementing informatics tools into complex clinical settings. You will demonstrate your mastery of these skills by completing practical application projects using real clinical data.
This specialization is supported by our industry partnership with Google Cloud. Thanks to this support, all learners will have access to a fully hosted online data science computational environment for free! Please note that you must have access to a Google account (i.e., gmail account) to access the clinical data and computational environment.
よくある質問
いつ講座や課題にアクセスできるようになりますか？
修了証に登録すると、すべてのビデオ、テスト、およびプログラミング課題（該当する場合）にアクセスできます。ピアレビュー課題は、セッションが開始してからのみ、提出およびレビューできます。購入せずにコースを検討することを選択する場合、特定の課題にアクセスすることはできません。
この専門講座をサブスクライブすると何を行うことができるようになりますか？
コースに登録する際、専門講座のすべてのコースにアクセスできます。コースの完了時には修了証を取得できます。電子修了証が成果のページに追加され、そこから修了証を印刷したり、LinkedInのプロフィールに追加したりできます。コースの内容の閲覧のみを希望する場合は、無料でコースを聴講できます。
返金ポリシーについて教えてください。
学資援助はありますか？
I live in an area that restricts access to Google products. Will I be able to complete the specialization?
Unfortunately at this time we can only allow students who have access to Google services (e.g., a gmail account) to complete the specialization. This is because we give students access to real clinical data and our privacy protections only allow data sharing through the Google BigQuery environment.
さらに質問がある場合は、受講者向けヘルプセンターにアクセスしてください。
コーセラは、世界トップクラスの教育を世界中に配信しています。
最高峰の大学や組織とのパートナーシップに基づくオンラインコースが提供されています。
© 2019 Coursera (株)すべての権利予約します。
